wordtoinv = 'unduly'
s = []
winner = ""
for ss in wn.synsets(wordtoinv):
for lemmas in ss.lemmas(): # all possible lemmas.
s.append(lemmas)
for pers in s:
posword = pers.pertainyms()[0].name()
if posword[0:3] == wordtoinv[0:3]:
winner = posword
break
print winner # undue
>>> from itertools import chain
>>> from nltk.corpus import wordnet as wn
>>> from difflib import get_close_matches as gcm
>>> possible_adjectives = [k.name for k in chain(*[j.pertainyms() for j in chain(*[i.lemmas for i in wn.synsets('terribly')])])]
['terrible', 'atrocious', 'awful', 'rotten']
>>> gcm('terribly',possible_adjectives)
['terrible']
计算possible_adjective的一种更具可读性的方法如下:
^{pr2}$
编辑:在新版本的NLTK中:
possible_adj = []
for ss in wn.synsets('terribly'):
for lemmas in ss.lemmas(): # all possible lemmas
for ps in lemmas.pertainyms(): # all possible pertainyms
possible_adj.append(ps.name())
正如MKoosej所提到的,nltk的引理不再是一个属性而是一个方法。我也做了一点简化,以得到最可能的单词。希望其他人也能使用它:
wordnet中有一个关系将
adjectives
连接到adverbs
,反之亦然。在计算
^{pr2}$possible_adjective
的一种更具可读性的方法如下:编辑:在新版本的NLTK中:
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